change to 0.24 and parametrize series tests

Author: reidy-p

doc/source/whatsnew/v0.23.0.txt

@@ -550,7 +550,6 @@ Other Enhancements
 - :func:`to_hdf` and :func:`read_hdf` now accept an ``errors`` keyword argument to control encoding error handling (:issue:`20835`)
 - :func:`cut` has gained the ``duplicates='raise'|'drop'`` option to control whether to raise on duplicated edges (:issue:`20947`)
 - :func:`date_range`, :func:`timedelta_range`, and :func:`interval_range` now return a linearly spaced index if ``start``, ``stop``, and ``periods`` are specified, but ``freq`` is not. (:issue:`20808`, :issue:`20983`, :issue:`20976`)
-- :func:`Series.mode` and :func:`DataFrame.mode` now support the ``dropna`` parameter which can be used to specify whether NaN/NaT values should be considered (:issue:`17534`)
 
 .. _whatsnew_0230.api_breaking:
 

doc/source/whatsnew/v0.24.0.txt

@@ -12,7 +12,7 @@ New features
 
 Other Enhancements
 ^^^^^^^^^^^^^^^^^^
--
+- :func:`Series.mode` and :func:`DataFrame.mode` now support the ``dropna`` parameter which can be used to specify whether NaN/NaT values should be considered (:issue:`17534`)
 -
 -
 

pandas/core/algorithms.py

@@ -809,7 +809,7 @@ def mode(values, dropna=True):
     dropna : boolean, default True
         Don't consider counts of NaN/NaT.
 
-        .. versionadded:: 0.23.0
+        .. versionadded:: 0.24.0
 
     Returns
     -------

pandas/core/arrays/categorical.py

@@ -2128,7 +2128,7 @@ def mode(self, dropna=True):
         dropna : boolean, default True
             Don't consider counts of NaN/NaT.
 
-            .. versionadded:: 0.23.0
+            .. versionadded:: 0.24.0
 
         Returns
         -------

pandas/core/frame.py

@@ -7057,7 +7057,7 @@ def mode(self, axis=0, numeric_only=False, dropna=True):
         dropna : boolean, default True
             Don't consider counts of NaN/NaT.
 
-            .. versionadded:: 0.23.0
+            .. versionadded:: 0.24.0
 
         Returns
         -------

pandas/core/series.py

@@ -1441,7 +1441,7 @@ def mode(self, dropna=True):
         dropna : boolean, default True
             Don't consider counts of NaN/NaT.
 
-            .. versionadded:: 0.23.0
+            .. versionadded:: 0.24.0
 
         Returns
         -------

pandas/tests/series/test_analytics.py

@@ -231,28 +231,34 @@ def test_median(self):
     ])
     def test_mode_empty(self, dropna, expected):
         s = Series([], dtype=np.float64)
-        tm.assert_series_equal(s.mode(dropna), expected)
+        result = s.mode(dropna)
+        tm.assert_series_equal(result, expected)
 
-    @pytest.mark.parametrize('dropna, expected1, expected2, expected3', [
-        (True, [1], [1, 3], [1.0]),
-        (False, [1], [1, 3], [1, np.nan]),
+    @pytest.mark.parametrize('dropna, data, expected', [
+        (True, [1, 1, 1, 2], [1]),
+        (True, [1, 1, 1, 2, 3, 3, 3], [1, 3]),
+        (False, [1, 1, 1, 2], [1]),
+        (False, [1, 1, 1, 2, 3, 3, 3], [1, 3]),
     ])
-    def test_mode_numerical(self, dropna, expected1, expected2, expected3):
-        data_single = [1] * 5 + [2] * 3
-        data_multi = [1] * 5 + [2] * 3 + [3] * 5
-
-        for dt in np.typecodes['AllInteger'] + np.typecodes['Float']:
-            s = Series(data_single, dtype=dt)
-            expected1 = Series(expected1, dtype=dt)
-            tm.assert_series_equal(s.mode(dropna), expected1)
-
-            s = Series(data_multi, dtype=dt)
-            expected2 = Series(expected2, dtype=dt)
-            tm.assert_series_equal(s.mode(dropna), expected2)
+    @pytest.mark.parametrize(
+        'dt',
+        list(np.typecodes['AllInteger'] + np.typecodes['Float'])
+    )
+    def test_mode_numerical(self, dropna, data, expected, dt):
+        s = Series(data, dtype=dt)
+        result = s.mode(dropna)
+        expected = Series(expected, dtype=dt)
+        tm.assert_series_equal(result, expected)
 
-        s = Series([1, np.nan])
-        expected = Series(expected3)
-        tm.assert_series_equal(s.mode(dropna), expected)
+    @pytest.mark.parametrize('dropna, expected', [
+        (True, [1.0]),
+        (False, [1, np.nan]),
+    ])
+    def test_mode_numerical_nan(self, dropna, expected):
+        s = Series([1, 1, 2, np.nan, np.nan])
+        result = s.mode(dropna)
+        expected = Series(expected)
+        tm.assert_series_equal(result, expected)
 
     @pytest.mark.parametrize('dropna, expected1, expected2, expected3', [
         (True, ['b'], ['bar'], ['nan']),
@@ -263,34 +269,37 @@ def test_mode_str_obj(self, dropna, expected1, expected2, expected3):
         data = ['a'] * 2 + ['b'] * 3
 
         s = Series(data, dtype='c')
+        result = s.mode(dropna)
         expected1 = Series(expected1, dtype='c')
-        tm.assert_series_equal(s.mode(dropna), expected1)
+        tm.assert_series_equal(result, expected1)
 
         data = ['foo', 'bar', 'bar', np.nan, np.nan, np.nan]
 
         s = Series(data, dtype=object)
-        expected2 = Series(expected2, dtype=object)
         result = s.mode(dropna).sort_values().reset_index(drop=True)
+        expected2 = Series(expected2, dtype=object)
         tm.assert_series_equal(result, expected2)
 
         data = ['foo', 'bar', 'bar', np.nan, np.nan, np.nan]
 
         s = Series(data, dtype=str)
+        result = s.mode(dropna)
         expected3 = Series(expected3, dtype=str)
-        tm.assert_series_equal(s.mode(dropna), expected3)
+        tm.assert_series_equal(result, expected3)
 
     @pytest.mark.parametrize('dropna, expected1, expected2', [
         (True, ['foo'], ['foo']),
         (False, ['foo'], [np.nan])
     ])
     def test_mode_mixeddtype(self, dropna, expected1, expected2):
-        expected = Series(expected1)
         s = Series([1, 'foo', 'foo'])
-        tm.assert_series_equal(s.mode(dropna), expected)
+        result = s.mode(dropna)
+        expected = Series(expected1)
+        tm.assert_series_equal(result, expected)
 
-        expected = Series(expected2, dtype=object)
         s = Series([1, 'foo', 'foo', np.nan, np.nan, np.nan])
         result = s.mode(dropna).sort_values().reset_index(drop=True)
+        expected = Series(expected2, dtype=object)
         tm.assert_series_equal(result, expected)
 
     @pytest.mark.parametrize('dropna, expected1, expected2', [
@@ -299,16 +308,18 @@ def test_mode_mixeddtype(self, dropna, expected1, expected2):
         (False, [np.nan], [np.nan, '2011-01-03', '2013-01-02']),
     ])
     def test_mode_datetime(self, dropna, expected1, expected2):
-        expected1 = Series(expected1, dtype='M8[ns]')
         s = Series(['2011-01-03', '2013-01-02',
                     '1900-05-03', 'nan', 'nan'], dtype='M8[ns]')
-        tm.assert_series_equal(s.mode(dropna), expected1)
+        result = s.mode(dropna)
+        expected1 = Series(expected1, dtype='M8[ns]')
+        tm.assert_series_equal(result, expected1)
 
-        expected2 = Series(expected2, dtype='M8[ns]')
         s = Series(['2011-01-03', '2013-01-02', '1900-05-03',
                     '2011-01-03', '2013-01-02', 'nan', 'nan'],
                    dtype='M8[ns]')
-        tm.assert_series_equal(s.mode(dropna), expected2)
+        result = s.mode(dropna)
+        expected2 = Series(expected2, dtype='M8[ns]')
+        tm.assert_series_equal(result, expected2)
 
     @pytest.mark.parametrize('dropna, expected1, expected2', [
         (True, ['-1 days', '0 days', '1 days'], ['2 min', '1 day']),
@@ -319,14 +330,16 @@ def test_mode_timedelta(self, dropna, expected1, expected2):
 
         s = Series(['1 days', '-1 days', '0 days', 'nan', 'nan'],
                    dtype='timedelta64[ns]')
+        result = s.mode(dropna)
         expected1 = Series(expected1, dtype='timedelta64[ns]')
-        tm.assert_series_equal(s.mode(dropna), expected1)
+        tm.assert_series_equal(result, expected1)
 
         s = Series(['1 day', '1 day', '-1 day', '-1 day 2 min',
                     '2 min', '2 min', 'nan', 'nan'],
                    dtype='timedelta64[ns]')
+        result = s.mode(dropna)
         expected2 = Series(expected2, dtype='timedelta64[ns]')
-        tm.assert_series_equal(s.mode(dropna), expected2)
+        tm.assert_series_equal(result, expected2)
 
     @pytest.mark.parametrize('dropna, expected1, expected2, expected3', [
         (True, Categorical([1, 2], categories=[1, 2]),
@@ -338,30 +351,35 @@ def test_mode_timedelta(self, dropna, expected1, expected2):
     ])
     def test_mode_category(self, dropna, expected1, expected2, expected3):
         s = Series(Categorical([1, 2, np.nan, np.nan]))
+        result = s.mode(dropna)
         expected1 = Series(expected1, dtype='category')
-        tm.assert_series_equal(s.mode(dropna), expected1)
+        tm.assert_series_equal(result, expected1)
 
         s = Series(Categorical([1, 'a', 'a', np.nan, np.nan]))
+        result = s.mode(dropna)
         expected2 = Series(expected2, dtype='category')
-        tm.assert_series_equal(s.mode(dropna), expected2)
+        tm.assert_series_equal(result, expected2)
 
         s = Series(Categorical([1, 1, 2, 3, 3, np.nan, np.nan]))
+        result = s.mode(dropna)
         expected3 = Series(expected3, dtype='category')
-        tm.assert_series_equal(s.mode(dropna), expected3)
+        tm.assert_series_equal(result, expected3)
 
     @pytest.mark.parametrize('dropna, expected1, expected2', [
         (True, [2**63], [1, 2**63]),
         (False, [2**63], [1, 2**63])
     ])
     def test_mode_intoverflow(self, dropna, expected1, expected2):
         # Test for uint64 overflow.
-        expected1 = Series(expected1, dtype=np.uint64)
         s = Series([1, 2**63, 2**63], dtype=np.uint64)
-        tm.assert_series_equal(s.mode(dropna), expected1)
+        result = s.mode(dropna)
+        expected1 = Series(expected1, dtype=np.uint64)
+        tm.assert_series_equal(result, expected1)
 
-        expected2 = Series(expected2, dtype=np.uint64)
         s = Series([1, 2**63], dtype=np.uint64)
-        tm.assert_series_equal(s.mode(dropna), expected2)
+        result = s.mode(dropna)
+        expected2 = Series(expected2, dtype=np.uint64)
+        tm.assert_series_equal(result, expected2)
 
     @pytest.mark.skipif(not compat.PY3, reason="only PY3")
     def test_mode_sortwarning(self):